Principles of inter-areal connections of the macaque cortex

The operation of real world networks is largely determined by their weighted and spatial characteristics. Surprisingly little is known about these features in cortex. We generated in macaque, a consistent database of inter-areal connections comprising projection densities (link weights) and physical lengths. Contrary to previous assumptions, the cortical connection matrix is dense (66%) and therefore, not a small-world graph. Link weights are both highly specific and heterogeneous and we show that it is these properties that characterize the network. The embedding of this weighted network is governed by a distance rule that predicts both its binary features as well as the global and local communication efficiencies. Analysis of the efficiency of this weighted network suggests that small changes in global communication efficiency are offset by large changes in local efficiency. These findings indicate a weight-based hierarchical layering in cortical architecture and processing.

[1]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[2]  C R Olson,et al.  Organization of cortical and subcortical projections to anterior cingulate cortex in the cat , 1988, The Journal of comparative neurology.

[3]  J W Scannell,et al.  On variability in the density of corticocortical and thalamocortical connections. , 2000, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[4]  C R Olson,et al.  Topographic organization of cortical and subcortical projections to posterior cingulate cortex in the cat: Evidence for somatic, ocular, and complex subregions , 1992, The Journal of comparative neurology.

[5]  C. Olson,et al.  Organization of cortical and subcortical projections to medial prefrontal cortex in the cat , 1988, The Journal of comparative neurology.

[6]  L. Garey.  Cortex: Statistics and Geometry of Neuronal Connectivity, 2nd edn. By V. BRAITENBERG and A. SCHÜZ. (Pp. xiii+249; 90 figures; ISBN 3 540 63816 4). Berlin: Springer. 1998. , 1999 .

[7]  G Tononi,et al.  Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. , 2000, Cerebral cortex.

[8]  Henry Kennedy,et al.  Quantitative Analysis of Connectivity in the Visual Cortex: Extracting Function from Structure , 2004, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[9]  Massimo Marchiori,et al.  Economic small-world behavior in weighted networks , 2003 .

[10]  Malcolm P. Young,et al.  Objective analysis of the topological organization of the primate cortical visual system , 1992, Nature.

[11]  R. Douglas,et al.  A Quantitative Map of the Circuit of Cat Primary Visual Cortex , 2004, The Journal of Neuroscience.

[12]  J. Hilbe.  Negative Binomial Regression: Preface , 2007 .

[13]  P. Roelfsema,et al.  Bottom-Up Dependent Gating of Frontal Signals in Early Visual Cortex , 2008, Science.

[14]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[15]  C. Gray,et al.  Adaptive Coincidence Detection and Dynamic Gain Control in Visual Cortical Neurons In Vivo , 2003, Neuron.

[16]  M P Young,et al.  Indeterminate Organization of the Visual System , 1996, Science.

[17]  B. Payne,et al.  Thalamic and cortical projections to middle suprasylvian cortex of cats: constancy and variation , 1997, Experimental Brain Research.

[18]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[19]  Jon H Kaas,et al.  The organization of sensory cortex , 2001, Current Opinion in Neurobiology.

[20]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[21]  A. Destexhe,et al.  Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. , 1999, Journal of neurophysiology.

[22]  M P Young,et al.  Anatomical connectivity defines the organization of clusters of cortical areas in the macaque monkey and the cat. , 2000, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[23]  H. Kennedy,et al.  Laminar Distribution of Neurons in Extrastriate Areas Projecting to Visual Areas V1 and V4 Correlates with the Hierarchical Rank and Indicates the Operation of a Distance Rule , 2000, The Journal of Neuroscience.

[24]  K. Rockland,et al.  Laminar origins and terminations of cortical connections of the occipital lobe in the rhesus monkey , 1979, Brain Research.

[25]  A. Díaz-Guilera,et al.  Efficiency of informational transfer in regular and complex networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  J. M. Hupé,et al.  Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons , 1998, Nature.

[27]  Peter Donnelly,et al.  Superfamilies of Evolved and Designed Networks , 2004 .

[28]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[29]  D. Watts.  Networks, Dynamics, and the Small‐World Phenomenon1 , 1999, American Journal of Sociology.